The mesoscale meteorological models Meso-NH and AROME

Transcription

1 The mesoscale meteorological models Meso-NH and AROME C.Lac (CNRM/GMME) For the Meso-NH community and the AROME team Nocturnal ozone in the residual layer over Marseille 85ppb «Astronomy meets Meteorology», September Marseille Parc Naturel Verdon

2 1. Introduction : General consideration on meteorological predictions 2. Overview of meso-scale models 3. Meso-NH : From meso-scales to Large Eddy Simulations. 4. The new operational meso-scale model AROME

3 Optical turbulence

4 Prognostic variables of the model are mean variables on the grid box

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6 2 2-2 km (LES) Mesoscale models All these kinds of models need different level of parametrization - Climate models and Global weather prediction : All the physics parametrized - Mesoscale models : Convection (deep) resolved. - Large Eddy Simulation. The most energetic eddies in turbulence are resolved, but it still needs to parameterize small-scale turbulence, radiation, microphysics.

7 Models Min. Resolution Spectral/ grid point Advection scheme Temporal scheme Time step MM5 PSU/NCAR RAMS Meso-NH WRF LM UM 1990 s MF/LA NCAR/MMM COSMO 2000 s UKMO AROME LES LES LES LES LES 1km 2.5km Up to 1km Grid Grid Grid Grid Grid Spectral Spectral Euler. Euler. Euler. Euler. Euler. SL SL Explicit LF Explicit LF Explicit LF Explicit Split For 2.5km 8s ( t=3 x ) For 2.5km 8s ( t=3 x ) For 2.5km 6-8s For 2.5km 15s Explicit Split For 2.5km 15s SI For 2.5km 60s MF SI For 2.5km 60s Nesting 2 way 2 way 2 way 2 way 2 way 1 way 1 way Turbulence scheme 1.5 closure 1D or 3D 1.5 closure 1D or 3D 1.5 closure 1D or 3D 2.5 closure 1D or 3D 2.5 closure 1D or 3D 1.5 closure 1D 1.5 closure 1D Microphysics Up to 6 species Up to 6 species Up to 6 species Up to 6 species Up to 6 species Up to 6 species Up to 6 species Data assimilation

8 Meso-NH model A research model, jointly developped by Meteo-France and Laboratoire d Aérologie (CNRS/UPS) 40 users laboratories 1. Recent improvements in the dynamics 2. Focus on the turbulence. Importance of the surface coupling.

9 t = 3500 s Horizontal wind 2D test case of orographic trapped waves Horizontal wind t = 5000 s Vertical velocity Previous advection schemes New advection schemes Turbulent Kinetic Energy Cloud A typical situation for optical turbulence T.Maric

10 Buoyancy effects

11 >0 in convective <0 in stable Closure : with, L=Mixing length Further details in E.Masciadri s presentation

12 CONVECTIVE BOUNDARY LAYER with LES Water vapor variability - Couvreux et al. (2005) Lidar observations at 12h LES simulation LES Simulations g/kg r v g/kg q v LES P3 aircraft KA aircraft.. max (pdf) _ min (pdf) at 0.5z i S(q v )<0 x= y= 100m, z<50m, t=7h

13 STABLE BOUNDARY LAYER Difficulty to simulate due to local circulations (drainage flows), intermittent effects (gravity waves), low level jets (LLJ). LES simulation of an observed LLJ during the Sables98 campaign Objective: study the mixing processes across the maximum of the wind of an observed Low-Level Jet (LLJ) using LES Duero river basin 100m tower applex = 6 m, appley = 4 m, applez = 2m (0 <z<100 m) and stretched above (applez = 5 m at about 400 m) Night: September 1998 M.A. Jiménez Universitat de les Illes Balears

14 Results (I): Mean profiles The maximum of the wind and the height are well captured The LLJ height coincides with the inversion height M.A. Jiménez Universitat de les Illes Balears The surface temperature obtained from the LES cools down much more than the observations

15 STABLE BOUNDARY LAYER : Comparison MesoNH/MM5 at meso-scale applex = 1km, applez min = 3m, 86 lev. A strongly stable night 23H 4H Obs. 23H 4H MM5 Meso-NH MNH MM5 U Bias O Rmse T Bias Rmse H 4H 23H 4H Bravo et al., 2008

16 On the importance of the surface coupling for the turbulence

17 The SURFEX (SURface Externalized) land surface scheme see P.Le Moigne s presentation

18 Atmospheric CO 2 modelling : May Boundary layer heterogeneity Zi = 1600m Forest : high sensible heat flux Sarrat et al.(2007a) Zi = 900m Agricultural area : low sensible heat flux

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20 A recent improvement in SURFEX: the CANOPY scheme (Masson, 2008) 1D Surface Boundary Layer scheme, with 6 added levels between the first atmospheric level and the surface An added term for U,, q, TKE T2m becomes pronostic

21 AROME (Applications of Research to Operations at MesoscalE) Almost-current operational meso-scale system (2.5km) with data assimilation (P.Brousseau s talk) - Dynamics : from ALADIN-NH - Physics : from Meso-NH

22 Objectives of AROME - Expected to improve heavy precipitation forecasts with strong emphasis on Mediterranean flash-floods - Prediction of local events (fog, breeze, urban effects, orographic) - Applications : chemistry, hydrology, fog, ocean, roads - A complex data assimilation system (further details in P.Brousseau s presentation) ALADIN Dx=10km Vertical levels = 40, Time step=60s Forecast range = 36h (1800s on 64 processors) AROME Dx=2,5km

23 Obs radar Diurnal convection (2)

24 Total cloudiness AROME 12 h vs Sat Vis Cloudiness

25 Model performance : low-level scores 25 objective scores of AROME- ( 30km ~ France using French automatic surface obs network (hourly data every MSL pressure Rmse Aladin Rmse Arome Bias Arome 10m windspeed Rmse Aladin Rmse Arome Bias Arome ( h ) forecast range 2m Temperature Bias Aladin Rmse Aladin Rmse Arome Bias Arome ( h ) forecast range Scores over France on 3 months Nov07-Jan08 (Arome in pink) Bias Aladin 2nd AROME training course, ( h ) forecast range Lisbon, March 2008 Bias Aladin

26 Meso-NH 1. A well-known research model with a broad range of resolution. Largely validated by the community. Large variety of applications for the Boundary layer. 2. Used for Optical Turbulence (Masciadri et al.) : C N ²=f (TKE, d /dz) AROME 1. Will be operational next month 2. Includes Meso-NH physics, a mesoscale data assimilation. Competitive computational time. 3. Perspective for Optical Turbulence : climatology, prediction

27 Thank you for your attention